The museum recognizes that the protection of life and limb override the protection of art. Performance based analysis was used to determine whether the fire safety features of the building met the design goals in accordance with 2016 California Building Code, 2016 California Fire Code, and 2018 NFPA 101. After completing the analysis of the museum, it was concluded, based on the available data acquired, that the building met the minimum requirements set forth in the building code, fire code, and national standards. The 2016 California Building Code, 2016 California Fire Code, and current National Fire Protection Association (NFPA) standards were used for the prescriptive based analysis. These four features were performed in order to determine whether the building complies with applicable codes and standards. Located in the northwest corner of San Francisco, SFMOMA houses a collection of over 33,000 works of painting, sculpture, design and media making it one of the largest museums in the world for modern and contemporary art.įour features of the prescriptive based analysis include: (1) Egress and Design Analysis, (2) Structural Fire Protection, (3) Water-Based Suppression System, and (4) Fire Alarm and Detection The SFMOMA is a 10-story, 460,000 𝑓𝑡2, Type 1A mixed occupancy building. A prescriptive and performance-based analysis was conducted on the San Francisco Museum of Modern Art. These behaviors can be designed to change over time.A fire and life safety analysis was conducted to fulfill a requirement for the Master of Science Degree in Fire Protection Engineering from California Polytechnic State University.
People may have different exit goals and may proceed to waypoints or wait before proceeding. Similarly, multiple different behaviors can be created and assigned to populations. For example, profiles representing children and adults can be created and then the population can be assigned to be 80% adult and 20% children. Parameters in each profile can be described using constant, uniform, standard normal, or log normal distributions. Multiple profiles can be created and assigned to different populations., which can even be dynamically switched based on behavior actions.
For example, people can dynamically avoid long queues or respond to the closure of doors. Based on their characteristics, each person uses their local environment to make decisions on exit paths. Each person in the model acts as an agent with their own profile (such as size and walking speed) and their own behavior (such as exits, waits, and waypoints).
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